# Store Clustering Retailigence Store Clustering Which of your stores are similar? What makes them similar? Is it affluence, demographics, weather, geography, or brand preferences? The answer is different for each category and is hidden in your data. The RETAILIGENCE machine learning can find these patterns without any effort or analysis by the users. Simply getting your store clusters right (from the customer’s perspective rather than the retailer’s ) can give your sales a boost by over 3% and set you on the path of a fully automated category management process driven by artificial intelligence. We don’t ask you for rules. Instead, we take your sales data at a basket level or at a summary level through our open API and give you the answers. You can review them through a very intuitive front end and make small changes if required. You don’t have to stay on top of changing customer shopping patterns because our machine learning algorithms will. The output is also accessible from the cloud through our APIs without any complex integration. **A POC for a few categories can be conducted with an excel extract of your data. Please get in touch if you would like a POC or wish to know more.** ### Would you like to critically assess your current process and get insights into what you can do going forward? Take this  brief quiz to help you evaluate where you stand in your journey towards a more customer-centric business process. ### CLUSTERING – THE KEY FIRST STEP TO CURATED CUSTOMER ASSORTMENTS HOW GOOD IS YOURS? Do you cluster considering 2 or more attributes together? _(e.g. store revenue, demographic profile, store grade, store size, competition, climate)_ Do you cluster by store size? YES NO Do you cluster by product attributes? _(e.g. brand, price position, material)_ YES NO Do you use data science to analyse patterns in detailed store/ sku level sales history to cluster like stores? YES NO Does your clustering provide unique insight into how your customers shop differently across your estate? YES NO